




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__ :   Tuple						=							logging.get_logger(__name__)

UpperCamelCase__ :   List[Any]						=							{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       List[Any]						=     "roberta"






				def __init__(			self      ,snake_case__=50265      ,snake_case__=768      ,snake_case__=12      ,snake_case__=12      ,snake_case__=3072      ,snake_case__="gelu"      ,snake_case__=0.1      ,snake_case__=0.1      ,snake_case__=512      ,snake_case__=2      ,snake_case__=0.02      ,snake_case__=1E-12      ,snake_case__=1      ,snake_case__=0      ,snake_case__=2      ,snake_case__="absolute"      ,snake_case__=True      ,snake_case__=None      ,**snake_case__      ,):
							super().__init__(pad_token_id=snake_case__      ,bos_token_id=snake_case__      ,eos_token_id=snake_case__      ,**snake_case__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      vocab_size
							SCREAMING_SNAKE_CASE_   : Any					  =      hidden_size
							SCREAMING_SNAKE_CASE_   : Tuple					  =      num_hidden_layers
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      num_attention_heads
							SCREAMING_SNAKE_CASE_   : Any					  =      hidden_act
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      intermediate_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      hidden_dropout_prob
							SCREAMING_SNAKE_CASE_   : Tuple					  =      attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE_   : Tuple					  =      max_position_embeddings
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      type_vocab_size
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      initializer_range
							SCREAMING_SNAKE_CASE_   : Tuple					  =      layer_norm_eps
							SCREAMING_SNAKE_CASE_   : str					  =      position_embedding_type
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      use_cache
							SCREAMING_SNAKE_CASE_   : Tuple					  =      classifier_dropout







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				@property
				def 		snake_case	(			self ):
							if self.task == "multiple-choice":
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
										SCREAMING_SNAKE_CASE_   : str					  =      {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ] )





"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)